Instructions to use Hudhayfah/TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hudhayfah/TestRepo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Hudhayfah/TestRepo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hudhayfah/TestRepo")
model = AutoModelForCausalLM.from_pretrained("Hudhayfah/TestRepo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hudhayfah/TestRepo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hudhayfah/TestRepo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hudhayfah/TestRepo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hudhayfah/TestRepo

SGLang

How to use Hudhayfah/TestRepo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Hudhayfah/TestRepo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hudhayfah/TestRepo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Hudhayfah/TestRepo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hudhayfah/TestRepo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hudhayfah/TestRepo with Docker Model Runner:
```
docker model run hf.co/Hudhayfah/TestRepo
```

TestRepo

File size: 1,147 Bytes

2139e54

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    "_name_or_path": "togethercomputer/RedPajama-INCITE-Chat-3B-v1",
    "architectures": [
        "GPTNeoXForCausalLM"
    ],
    "attention_dropout": 0.0,
    "bos_token_id": 0,
    "classifier_dropout": 0.1,
    "eos_token_id": 0,
    "hidden_act": "gelu",
    "hidden_dropout": 0.0,
    "hidden_size": 2560,
    "initializer_range": 0.02,
    "intermediate_size": 10240,
    "layer_norm_eps": 1e-05,
    "max_position_embeddings": 2048,
    "model_type": "gpt_neox",
    "num_attention_heads": 32,
    "num_hidden_layers": 32,
    "rope_scaling": null,
    "rotary_emb_base": 10000,
    "rotary_pct": 1.0,
    "tie_word_embeddings": false,
    "torch_dtype": "float16",
    "transformers_version": "4.32.0",
    "use_cache": true,
    "use_parallel_residual": false,
    "vocab_size": 50432,
    "pretraining_tp": 1,
    "pad_token_id": 0,
    "quantization_config": {
        "bits": 4,
        "group_size": 128,
        "damp_percent": 0.1,
        "desc_act": false,
        "sym": true,
        "true_sequential": true,
        "model_name_or_path": null,
        "model_file_base_name": "model",
        "quant_method": "gptq"
    }
}